Joel O. Paz

Joel O. Paz

Associate Professor

Environmental Engineering, Crop Modeling, Decision Support Systems
AETB Undergraduate Coordinator
Agricultural and Biological Engineering

Office: 253 Agricultural and Biological Engineering
jpaz@abe.msstate.edu
P 662.325.4798
Google Scholar Profile
ResearchGate Profile
Joel O. Paz's Personal Website

Education

Ph.D., Agricultural Engineering and Water Resources (co-majors), Iowa State University.

Experience Record

  • July 2014 – Present, Associate Professor (Tenured), Mississippi State University, Mississippi State, MS
  • August 2009 – June 2014, Assistant Professor, Mississippi State University, Mississippi State, MS
  • March 2005 – August 2009, Public Service Assistant, The University of Georgia, Griffin, GA

Specialty Areas

Dr. Paz’s research program at Mississippi State University focuses on water quality, water resources, GIS, and impacts of climate change. He has worked extensively in the areas of environmental quality, sustainable agriculture, impacts of climate variability and climate change, decision support tools for risk management, and agricultural applications of climate and weather information through projects funded by USDA, DOE, NASA, NOAA, NSF, and commodity groups.

Research Interests

  • The two most important issues affecting the sustainability of agroecosystems in the Mississippi Delta region are declining groundwater levels in the Mississippi Delta Shallow Alluvial Aquifer and nutrient loads into the Mississippi River and the Gulf of Mexico. Current Federal programs, such as the Mississippi River Basin Healthy Watersheds Initiative, provide support for farmers to implement new conservation practices such as on-farm water storage systems to reduce nutrient loading and improve water quality in the Basin and Gulf of Mexico. Water storage reservoirs offer farmers and landowners the practical benefits of providing supplemental surface water for irrigation while also capturing nutrient-rich tailwater from irrigated fields. Quantifying the long-term effects of these conservation practices on water-use, water quality, and agricultural production is very important in order to develop appropriate strategies for the adaptation of these practices.
  • A key component of the TMDL process is monitoring the water bodies for water quality. Remote sensing can provide a quick and cost-effective way to identify impaired water bodies and critical source areas, enabling researchers to assess the extent of impairment of specific segments of streams and rivers. In addition, remote sensing can be used as part of a large-scale water quality monitoring program and can provide information to environmental quality regulating agencies that can be used to develop management plans to reduce movement of pollutants into water bodies.
  • The use of climate and weather information is essential in addressing problems in agroecosystems and water resources. Applications include synoptic weather forecasting and web-based information delivery systems for managing crop disease risk, predicting the impacts of pests and diseases on crop yield, reducing vulnerability to drought, and development of strategies to mitigate the impacts of climate variability and climate change.
  • Increased crop production and expansion of irrigated acreage in the Southeastern USA have increased agricultural water use during the past three decades. Crop simulation models using downscaled regional climate data can be used to estimate future water demand for irrigation under different climate scenarios.
  • The sustainability of bioenergy produced from a particular source may be measured using net energy value (NEV), an established measure for the energy gain and sustainability of bioethanol. The sustainability of different feedstocks such as switchgrass and giant miscanthus, can be examined in terms of the net energy value and CO2 emissions.
  • Agricultural and forestry logging residues, are potential feedstocks for Combined Heat and Power (CHP) production. A project was conducted to assess the use of corn stover and forest logging residue as distributed feedstock sources for CHP facilities in Mississippi.

Publications

*Postdoc; Graduate student supervised by Dr. Paz

  • Guzman, S.M., J.O. Paz, M.L.M. Tagert, and A.E. Mercer. 2018. Evaluation of seasonally classified inputs for the prediction of daily groundwater levels: NARX networks vs. support vector machines. Environmental Modeling and Assessment. https://doi.org/10.1007/s10666-018-9639-x
  • Karki, R., M.L.M. Tagert, and J.O. Paz. 2018. Evaluating the Nutrient Reduction and Water Supply Benefits of an On-Farm Water Storage (OFWS) System in East Mississippi. Agriculture, Ecosystems and Environment. 265: 476-487. https://doi.org/10.1016/j.agee.2018.06.024
  • Amin, A., W. Nasim, S. Fahad, S. Ali, S. Ahmad, A. Rasool, N. Saleem, H.M. Hammad, S.R. Sultana, M. Mubeen, H.F. Bakhat, N. Ahmad, G.M. Shah, M. Adnan, M. Noor, A. Basir, S. Saud, M.H. Rahman, and J.O. Paz. 2018. Evaluation and analysis of temperature for historical (1996–2015) and projected (2030–2060) climates in Pakistan using SimCLIM climate model: Ensemble application.  Atmospheric Research 213:422-436 https://doi.org/10.1016/j.atmosres.2018.06.021
  • Guzmán, S., J.O. Paz, M.L.M. Tagert, and J.W. Pote. 2018. An Integrated SVR and Crop Model to Estimate Daily Groundwater Level. Agricultural Systems 159:248-259. https://doi.org/10.1016/j.agsy.2017.01.017
  • Karki, R., M.L. Tagert, J.O. Paz, and R.L. Bingner. 2017. Application of AnnAGNPS to model an agricultural watershed in East-Central Mississippi for the evaluation of an on-farm water storage (OFWS) system. Ag. Water Manage. 192:103-114. https://doi.org/10.1016/j.agwat.2017.07.002)
  • Perez-Gutierrez, J.D., J.O. Paz, and M.L. Tagert. 2017. Seasonal Water Quality Changes in On-Farm Water Storage Systems in a South-Central U.S. Agricultural Watershed. Agricultural Water Management 187:131-139. https://doi.org/10.1016/j.agwat.2017.03.014
  • Kisekka, I., K. DeJonge, L. Ma, J.O. Paz, and K. Mankin-Douglas. 2017. Crop Modeling Applications in Agricultural Water Management. Transactions of the ASABE 60(6):1959-1964. https://doi.org/10.13031/trans.12693
  • Ouyang, Y., J.O. Paz, G. Feng, J. Read, A. Adeli, and J. Jenkins. 2017. A Model to Estimate Hydrological Processes and Water Budget in an Irrigation Farm Pond. Water Resources Management 31:2225-2241. https://doi.org/10.1007/s11269-017-1639-0.
  • Guzmán, S., J.O. Paz, and M.L. Tagert. 2017. The use of NARX neural networks to forecast daily groundwater levels. Water Resources Management. https://doi.org/10.1007/s11269-017-1598-5
  • Bao, Y., G. Hoogenboom, R.W. McClendon, and J.O. Paz. 2015. Potential adaptation strategies for rainfed soybean production in the south-eastern USA under climate change based on the CSM-CROPGRO-Soybean model. The Journal of Agricultural Science, available on CJO2015. https://doi.org/10.1017/S0021859614001129.
  • Woli*, P. and J.O. Paz. 2015. Crop management effects on the energy and carbon balances of maize stover-based ethanol Production. Energies 2015, 8(1), 278-303. https://doi.org/10.3390/en8010278.
  • Thorp, K.R., S. Ale, M.P. Bange, E.M. Barnes, G. Hoogenboom, R.J. Lascano, A.C. McCarthy, S. Nair, J.O. Paz, N. Rajan, K.R. Reddy, G.W. Wall, and J.W. White. 2014. Development and application of process-based simulation models for cotton production: A review of past, present, and future directions. Journal of Cotton Science 18:10-47
  • Radhakrishnan, S., J.O. Paz, F. Yu, S. Eksioglu, and D.L. Grebner. 2013. Assessment of potential capacity increases at combined heat and power facilities based on available corn stover and forest logging residue. Energies (6): 4418-4428. https://doi.org/10.3390/en6094418
  • Woli*, P. and J.O. Paz. 2013. Biomass yield and utilization rate effects on the sustainability and environment-friendliness of maize stover- and switchgrass-based ethanol production. International Journal of Environment and Bioenergy 7(1): 28-42.
  • Woli*, P., J.O. Paz, G. Hoogenboom, A. Garcia y Garcia, and C.W. Fraisse. 2013. The ENSO effect on peanut yield as influenced by planting date and soil type. Agricultural Systems 121 (Oct 2013):1-8. https://doi.org/10.1016/j.agsy.2013.06.005
  • Woli*, P., J.W. Jones, K.T. Ingram, and J.O. Paz. 2013. Forecasting drought using the Agricultural Reference Index for Drought (ARID): a case study. Weather and Forecasting 28(2) April 2013: 427-443.  https://doi.org/10.1175/WAF-D-12-00036.1
  • Woli*, P. J.O. Paz, D.J. Lang, B.S. Baldwin, and J.R. Kiniry. 2012. Soil and variety effects on the energy and carbon balances of switchgrass-derived ethanol. Journal of Sustainable Bioenergy Systems 2012(2): 65-74. https://doi.org/10.4236/jsbs.2012.24010
  • Paz, J.O., P. Woli, A. Garcia y Garcia and G. Hoogenboom. 2012. Cotton yields as influenced by ENSO at different planting dates and spatial aggregation levels. Agricultural Systems 111 (Sept 2012):45-52. https://doi.org/10.1016/j.agsy.2012.05.004
  • Chevalier, R.F., G. Hoogenboom, R.W. McClendon, and J.O. Paz. 2012. A web-based fuzzy expert system for frost warnings in horticultural crops. Environmental Modeling and Software 35 (July 2012):84-91. https://doi.org/10.1016/j.envsoft.2012.02.010
  • Woli*, P. and J.O. Paz. 2012. Evaluation of various methods of estimating global solar radiation in the Southeastern USA. J. Appl. Meteor. Climatol. 51(5):972-985. https://doi.org/10.1175/JAMC-D11-0141.1
  • Salazar-Gutierrez, M.R., J.E. Hook, A. Garcia y Garcia, J.O. Paz, B. Chaves and G. Hoogenboom. 2012. Estimating irrigation water use for maize in the Southeastern USA: a modeling approach. Agricultural Water Management 107:104-111 https://doi.org/10.1016/j.agwat.2012.01.015
  • Olatinwo, R.O., T.Prahba, J.O. Paz, and G. Hoogenboom. 2012.  Predicting favorable conditions for early leaf spot of peanut using output from the Weather Research and Forecasting (WRF) model.  Int. J. Biometeorol. 56(2):259-268. https://doi.org/10.1007/s00484-011-0425-6.
  • Olatinwo, R.O., T.Prahba, O. Paz, D. Riley, and G. Hoogenboom. 2011. The Weather Research and Forecasting (WRF) model: application in prediction of TSWV-vectors populations. Journal of Applied Entomology 135(1-2):81-90. https://doi.org/10.1111/j.1439-0418.2010.01539.x
  • Chevalier, R.F., G. Hoogenboom, R.W. McClendon, and J.O. Paz. 2011. Support vector regression with reduced training sets for air temperature prediction: a comparison with artificial neural networks. Neural Computing and Applications 20(1):151-159. https://doi.org/10.1007/s00521-010-0363-y
  • Panda, S.S., G. Hoogenboom, and J.O. Paz. 2010. Remote sensing and geospatial technological applications for site-specific management of fruit and nutcrops: a review. Remote Sensing 2(8): 1973-1997. https://doi.org/10.3390/rs2081973.
  • Olatinwo*, R.O., J.O. Paz, R.C. Kemerait, Jr., A.K. Culbreath, and G. Hoogenboom.  2010. El Niño-Southern Oscillation (ENSO) impact on tomato spotted wilt intensity in peanut and the implication on yield.  Crop Protection 29(5):448-453. https://doi.org/10.1016/j.cropro.2009.10.014
  • Persson, T., A. Garcia y Garcia, J.O. Paz, C.W. Fraisse and G. Hoogenboom. 2010. Reduction in greenhouse gas emissions due to the use of bio-ethanol from wheat grain and straw produced in the southeastern USA. Jour. of Agric. Science 148(5):511-527. https://doi.org/10.1017/S0021859610000316
  • Persson, T., A. Garcia y Garcia, J.O. Paz, B.V. Ortiz, and G. Hoogenboom. 2010. Simulating the production potential and net energy yield of maize-ethanol in the southeastern USA. European Journal of Agronomy 32(4):272-279. https://doi.org/10.1016/j.eja.2010.01.004.
  • Garcia y Garcia, A., T. Persson, J.O. Paz, C. Fraisse, and G. Hoogenboom. 2010. ENSO-based climate variability affects water use efficiency of rainfed cotton grown in the southeastern USA. Agriculture, Ecosystems and Environment 139(4):629-635. https://doi.org/10.1016/j.agee.2010.10.009.
  • Crane, T.A., C. Roncoli, J.O. Paz, N.E. Breuer, K. Broad, and G. Hoogenboom. 2010. Forecast skill and farmer’s skill: Seasonal climate forecasts and agricultural risk management in the southeastern United States. Weather, Climate, and Society 2(1):44-59.
    https://doi.org/10.1175/2009WCAS1006.1
  • Olatinwo*, R.O., J.O. Paz, S.L. Brown, R.C. Kemerait, Jr., A.K. Culbreath, and G. Hoogenboom.  2009. Impact of early spring weather factors on the risk of tomato spotted wilt in peanut.  Plant Disease 93(8):783-788. https://doi.org/10.1094/PDIS-93-8-0783.
  • Persson, T., A. Garcia y Garcia, J.O. Paz, J.W. Jones, and G. Hoogenboom. 2009. Net energy value of maize ethanol as a response to different climate and soil conditions in the southeastern USA. Biomass and Bioenergy 33(8):1055-1064.  https://doi.org/10.1016/j.biombioe.2009.03.007
  • Panda, S., G. Hoogenboom, and J.O. Paz. 2009. Distinguishing blueberry bushes from mixed vegetation land use with applied geospatial technology. Computers and Electronics in Agriculture 57(1-2):51-58. https://doi.org/10.1016/j.compag.2009.02.007
  • Persson, T., A. Garcia y Garcia, J.O. Paz, J.W. Jones, and G. Hoogenboom. 2009. Maize ethanol feedstock production and net energy value as affected by climate variability and crop management practices. Agricultural Systems 100(2009):11-21. https://doi.org/10.1016/j.agsy.2008.11.004
  • Olatinwo*, R.O, J.O. Paz, S.L. Brown, R.C. Kemerait, A.K. Culbreath, J.P. Beasley, Jr., and G. Hoogenboom. 2008. A predictive model for spotted wilt epidemics in peanut based on local weather conditions and the tomato spotted wilt virus risk index. Phytopathology 98(10): 1066-1074.
  • Thorp, K.R., K.C. DeJonge, A.L. Kaleita, W.D. Batchelor, and J.O. Paz. 2008.  Methodology for the use of DSSAT models for precision agriculture decision support. Computers and Electronics in Agriculture 64(2):276-285.
  • Shank, D.B., R.W. McClendon, J.O. Paz, and G. Hoogenboom. 2008. Ensemble artificial intelligence for prediction of dew point temperature. Applied Artificial Intelligence 22 (6):523-542.
  • Paz, J.O., W. Fraisse, L.U. Hatch, A. Garcia y Garcia, L.C. Guerra, O. Uryasev, J.G. Bellow, J.W. Jones, and G. Hoogenboom.  2007.  Development of an ENSO-based irrigation decision support tool for peanut production in the Southeastern US.  Computers and Electronics in Agriculture 55(1):28-35.
  • Thorp, K.R., W.D. Batchelor, J.O. Paz, A.L. Kaleita, and K.C. Dejonge. 2007.  Using cross validation to evaluate CERES-Maize yield simulations within a decision support system for precision agriculture.  Transactions of the ASABE 50(4):1467-1479.
  • Garcia y Garcia, A., G. Hoogenboom, L.C. Guerra, J.O. Paz, and C.W. Fraisse. 2006. Analysis of the interannual variation of peanut yield in Georgia using a dynamic crop simulation model. Transactions of the ASABE 49(6):2005-2015.
  • Irmak, A., J.W. Jones, W.D. Batchelor, S. Irmak, K.J. Boote, and J.O. Paz. 2006.  Artificial neural network model as a data analysis tool in precision farming.  Transactions of the ASABE 49(6): 2027-2037.
  • Fraisse, C.W., N.E. Breuer, D. Zierden, J.G. Bellow, J.O. Paz, V.E. Cabrera, A. Garcia y Garcia, K.T. Ingram, L.U. Hatch, G. Hoogenboom, J.W. Jones and J.J. O'Brien. 2006.  AgClimate: A climate forecast information system for agricultural risk management in the southeastern USA.  Computers and Electronics in Agriculture. 53(2006):13-27.
  • Irmak, A., J.W. Jones, W.D. Batchelor, S. Irmak, J.O. Paz, and K.J. Boote. 2006.  Analysis of spatial yield variability using a combined crop model-empirical approach.  Transactions of the ASABE 49(3):811-818.
  • Thorp, K.R., W.D. Batchelor, J.O. Paz, B.L. Steward, and P.C. Caragea.  2006.  Methodology to link production and environmental risks of precision nitrogen management strategies in corn.  Agricultural Systems 89(2-3):272-298. doi:10.1016/j.agsy.2005.09.005
  • Miao, Y., J. Mulla, W.D. Batchelor, J.O. Paz, P.C. Robert, and J. A. Hernandez.  2006.  Evaluating management zone optimal nitrogen rates with a crop growth model.  Agron. J. 98(3):545-553. doi:10.2134/agronj2005.0153
  • Paz, J.O., W.D. Batchelor, and P. Pedersen.   WebGro: a web-based soybean management decision support system.  Agron. J. 96(6):1771-1779.  doi:10.2134/agronj2004.1771
  • Seidl, M.S., W.D. Batchelor, and J.O. Paz. 2004.  Integrating remote images with crop models to improve spatial yield predictions for soybeans.  Transactions of the ASAE 47(6):2081-2090.
  • Paz, J.O., W.D. Batchelor, and J.W. Jones.   Estimating potential economic return for variable rate soybean variety management.  Transactions of the ASAE 46(4): 1225–1233.
  • Batchelor, W.D., B.Basso, and J.O. Paz. 2002.  Strategies to analyze spatial and temporal yield variability using crop models.  European Journal of Agronomy 18(2002):141-158.
  • Paz, J.O.,D. Batchelor, and D.G. Bullock. 2002.  Procedure to use a crop model to identify water stressed areas in soybean fields using on-farm data.  Applied Engineering in Agriculture 18(5):643-646.
  • Irmak, A, D. Batchelor, J.W.Jones, S. Irmak, J.O. Paz, and H. Beck.  2002. Relationship between plant available soil water and yield for explaining within-field soybean yield variability. Applied Engineering in Agriculture 18(4):471-482.
  • Irmak, A., J.W. Jones, W.D. Batchelor, and J.O. Paz. 2002.  Linking multiple layers of information for attribution of causes of spatial yield variability in soybean.  Transactions of the ASAE 45(3):839-849.
  • Fallick, J.B., W.D. Batchelor, G.L. Tylka, T. Niblack, and J.O. Paz. 2002.  Coupling soybean cyst nematode damage to CROPGRO-Soybean.  Transactions of the ASAE 45(2):433-441.
  • Irmak, A., J.W. Jones, W.D. Batchelor, and J.O. Paz. 2001.  Estimating spatially variable soil properties for application of crop models in precision farming.  Transactions of the ASAE 44(5):1343-1353
  • Booltink, H.W.G., B.J. Van Alphen, W.D. Batchelor, J.O. Paz, J.J. Stoorvogel, and R.Vargas.  2001.  Tools for optimizing management of spatially variable fields.  Agricultural Systems 70:445-476.
  • Paz, J.O., W.D. Batchelor, G.L. Tylka, and Hartzler. 2001. A modeling approach to quantifying the effects of spatial soybean yield limiting factors. Transactions of the ASAE 44(5):1329-1334.
  • Paz, J.O., W.D. Batchelor, and G.L. Tylka. 2001. Method to use crop growth models to estimate potential return for variable-rate management in soybeans. Transactions of the ASAE 44(5): 1335-1341.
  • Seidl, M.S., W.D. Batchelor, J.B. Fallick, and J.O. Paz. 2001.  GIS-Model based decision support system to evaluate corn and soybean prescriptions. Applied Engineering in Agriculture 17(5):721-728.
  • Paz, J.O., W.D. Batchelor, T.S. Colvin, S.D. Logsdon, T.C. Kaspar, D.L. Karlen, and B.A. Babcock. 1999. Model-based techniques to determine variable rate nitrogen for corn. Agricultural Systems 61(1999):69-75.
  • Paz, J.O., W.D. Batchelor, T.S. Colvin, S.D. Logsdon, T.C. Kaspar, and D.L. Karlen. 1998. Analysis of water stress effects causing spatial yield variability in soybeans. Transactions of the ASAE 41(5):1527-1534.

Awards and Honors

  • CALS Excellence in Undergraduate Teaching Award – Lower Division,
    College of Agriculture and Life Sciences, Mississippi State University, March 27, 2018
  • Teaching Award of Merit, Gamma Sigma Delta - The Honor Society of Agriculture,
    Mississippi State University, April 26, 2017
  • Honored as Visiting Professor, U.S. Agency for International Development (USAID) Science, Technology, Research, and Innovation for Development (STRIDE) Program. Mariano Marcos State University, Batac, Ilocos Norte, Philippines. July 12, 2016
  • Certificate of Appreciation as External Reviewer of Undergraduate Curricula. College of Engineering and Agro-Industrial Technology, University of the Philippines Los Baños, Laguna, Philippines. November 23, 2015
  • Outstanding Achievement Award. Southeast Climate Consortium. March 21, 2008
  • ASABE IET Select Paper Award. 2005 ASABE International Meeting. Tampa, FL.
  • ASABE IET Select Paper Award. 2003 ASABE International Meeting. Las Vegas, NV.
  • ASABE IET Select Paper Award. 2001 ASABE International Meeting. Sacramento, CA.
  • ASABE IET Select Paper Award. 2000 ASABE International Meeting. Milwaukee, WI.